2.b) Explain any two big data applications
Answer:
Note: Explain any two.
Big Data Applications:
Big Data in Marketing and Sales
- Data are important for most aspect of marketing, sales and advertising.A definition of marketing is the creation, communication and delivery of value to customers. Customer (desired) value means what a customer desires from a product. Customer (perceived) value means what the customer believes to have received from a product after purchase of the product. Customer value analytics (CVA) means analyzing what a customer really needs. CVA makes it possible for leading marketers, such as Amazon to deliver the consistent customer experiences.
- An example of fraud is borrowing money on already mortgage assets. Example of timely compliances means returning the loan and interest installments by the borrowers.
- A few examples in service-innovation are as follows: A company develops software and then offers services like Uber. Another example is of a company which develops software for hiring services, and then offers costly construction machinery and equipment.
- Big data is providing marketing insights into (i) most effective content at each stage of a sales cycle, (ii) investment in improving the customer relationship management (CRM), (iii) addition to strategies for increasing customer lifetime value (CLTV), (iv) lowering of customer acquisition cost (CAC). Cloud services use Big Data analytics for CAC, CLTV and other metrics, the essentials in any cloud-based business
- Contextual marketing means using an online marketing model in which a marketer sends to potential customers the targeted advertisements, which are based on the search terms during latest browsing patterns usage by customers.
Big data Analytics in detection of marketing Fruads:
Fraud detection is vital to prevent financial loss to users. Transferring customer information to third party, falsifying company information to financial institutions, marketing product with compromising quality, marketing product with service level different from the promised, stealing intellectual property, and much more.
Big Data analytics enable fraud detection. Big Data usages has the following features-for enabling detection and prevention of frauds:
- Fusing of existing data at an enterprise data warehouse with data from sources such as social media, websites, blogs, e-mails, and thus enriching existing data
- Using multiple sources of data and connecting with many applications
- Providing greater insights using querying of the multiple source data
- Analyzing data which enable structured reports and visualization
- Providing high volume data mining, new innovative applications and thus leading to new business intelligence and knowledge discovery
- Making it less difficult and faster detection of threats, and predict likely frauds by using various data and information publicly available.
Big Data Risks
Large volume and velocity of Big Data provide greater insights but also associate risks with the data used Data included may be erroneous, less accurate or far from reality. Analytics introduces new errors due to such data. Companies need to take risks of using Big Data and design appropriate risk management procedures. They have to implement robust risk management processes and ensure reliable predictions. Corporate, society and individuals must act with responsibility.
Big Data Credit Risk Management
Financial institutions, such as banks, extend loans to industrial and household sectors. These institutions in many countries face credit risks, mainly risks of (i) loan defaults, (ii) timely return of interests and principal amount.
Financing institutions are keen to get insights into the following:
- Identifying high credit rating business groups and individuals,
- Identifying risk involved before lending money
- Identifying industrial sectors with greater risks
- Identifying types of employees and businesses with greater risks
- Anticipating liquidity issues (availability of money for further issue of credit and rescheduling credit over the years.
Big Data and Healthcare
Big Data analytics in health care use the following data sources:
- clinical records,
- pharmacy records,
- electronic medical records
- diagnosis logs and notes and
- additional data, such as deviations from person usual activities, medical leaves from job, social interactions.
Healthcare analytics using Big Data can facilitate the following:
- Provisioning of value-based and customer-centric healthcare,
- Utilizing the Internet of Things for health care
- Preventing fraud, waste, abuse in the healthcare industry and reduce healthcare costs.
- Improving outcomes
- Monitoring patients in real time.
Big Data in Medicine
Big Data analytics deploys large volume of data to identify and derive intelligence using predictive models about individuals. Big Data driven approaches help in research in medicine which can help patients. Big Data offers potential to transform medicine and the healthcare system.
- Aggregating large volume and variety of information around from multiple sources the DNAS, proteins, and metabolites to cells, tissues, organs, organisms, and ecosystems, that can enhance the understanding of biology of diseases. Big data creates patterns and models by data mining and help in better understanding and research,
- Deploying wearable devices data, the devices data records during active as well as inactive periods, provide better understanding of patient health, and better risk profiling the user for certain diseases.
Big Data in Advertising
The impact of Big Data is tremendous on the digital advertising industry. The digital advertising industry sends advertisements using SMS, e-mails, WhatsApp, LinkedIn, Facebook, Twitter and other mediums. Big data real time analytics for faster insights, emerging trends and patterns, and gain actionable insights for facing competitions from similar products in digital advertising and building relationships
Big Data captures data of multiple sources in large volume, velocity and variety of data unstructured and enriches the structured data at the enterprise data warehouse. Big data real time analytics provide emerging trends and patterns, and gain actionable insights for facing competitions from similar products. The data helps digital advertisers to discover new relationships, lesser competitive regions and areas.
Success from advertisements depend on collection, analyzing and mining. The new insights enable the personalization and targeting the online, social media and mobile for advertisements called hyper-localized advertising.